faster rcnn test demo ---repaired for video input and save the image, label, score et al. into .mat format
function script_faster_rcnn_demo()
close all;
clc;
clear mex;
clear is_valid_handle; % to clear init_key
run(fullfile(fileparts(fileparts(mfilename('fullpath'))), 'startup'));
%% -------------------- CONFIG --------------------
opts.caffe_version = 'caffe_faster_rcnn';
opts.gpu_id = auto_select_gpu;
active_caffe_mex(opts.gpu_id, opts.caffe_version);
opts.per_nms_topN = 6000;
opts.nms_overlap_thres = 0.7;
opts.after_nms_topN = 300;
opts.use_gpu = true;
opts.test_scales = 600;
% %% -------------------- INIT_MODEL --------------------
% model_dir = fullfile(pwd, 'output', 'faster_rcnn_final', 'faster_rcnn_VOC0712_vgg_16layers'); %% VGG-16
model_dir = fullfile(pwd, 'output', 'faster_rcnn_final', 'faster_rcnn_VOC0712_ZF'); %% ZF
proposal_detection_model = load_proposal_detection_model(model_dir);
proposal_detection_model.conf_proposal.test_scales = opts.test_scales;
proposal_detection_model.conf_detection.test_scales = opts.test_scales;
if opts.use_gpu
proposal_detection_model.conf_proposal.image_means = gpuArray(proposal_detection_model.conf_proposal.image_means);
proposal_detection_model.conf_detection.image_means = gpuArray(proposal_detection_model.conf_detection.image_means);
end
% caffe.init_log(fullfile(pwd, 'caffe_log'));
% proposal net
rpn_net = caffe.Net(proposal_detection_model.proposal_net_def, 'test');
rpn_net.copy_from(proposal_detection_model.proposal_net);
% fast rcnn net
fast_rcnn_net = caffe.Net(proposal_detection_model.detection_net_def, 'test');
fast_rcnn_net.copy_from(proposal_detection_model.detection_net);
% set gpu/cpu
if opts.use_gpu
caffe.set_mode_gpu();
else
caffe.set_mode_cpu();
end
%% -------------------- WARM UP --------------------
% the first run will be slower; use an empty image to warm up
for j = 1:2 % we warm up 2 times
im = uint8(ones(375, 500, 3)*128);
if opts.use_gpu
im = gpuArray(im);
end
[boxes, scores] = proposal_im_detect(proposal_detection_model.conf_proposal, rpn_net, im);
aboxes = boxes_filter([boxes, scores], opts.per_nms_topN, opts.nms_overlap_thres, opts.after_nms_topN, opts.use_gpu);
if proposal_detection_model.is_share_feature
[boxes, scores] = fast_rcnn_conv_feat_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
rpn_net.blobs(proposal_detection_model.last_shared_output_blob_name), ...
aboxes(:, 1:4), opts.after_nms_topN);
else
[boxes, scores] = fast_rcnn_im_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
aboxes(:, 1:4), opts.after_nms_topN);
end
end
%% -------------------- TESTING --------------------
% im_names = {'001763.jpg', '004545.jpg', '000542.jpg', '000456.jpg', '001150.jpg'};
% these images can be downloaded with fetch_faster_rcnn_final_model.m
% for j = 1:length(im_names)
% im = imread(fullfile(pwd, im_names{j}));
%% -------------------------- Video ------------------
%% initialization
video_names = {};
%% --------------path for test ------------
path = '/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/video/test_video/';
savePath = '/media/wangxiao/247317a3-e6b5-45d4-81d1-956930526746/video/test_video_images/';
%% ---------------path for the 4TB drive----------
% path = '/media/wangxiao/Seagate/pedestrian data/';
% savePath = '/media/wangxiao/Seagate/pedestrian data/pedestrian frame/';
% video_dir = dir(path);
video_dir = dir(fullfile(path, '*.avi'));
% if isempty(video_dir)
% video_dir = dir(fullfile(path, '*.mp4'));
% end
% if isempty(video_dir)
% video_dir = dir(fullfile(path, '*.rmvb'));
% end
% if isempty(video_dir)
% video_dir = dir(fullfile(path, '*.mkv'));
% end
%%
k =1;
for i = 1:length(video_dir)
if ~video_dir(i).isdir
video_names{k} = video_dir(i).name; % 视频的名字 保存在video_name中；
k = k+1;
end
end
clear k;
running_time = [];
%% 将视频load进来，切割为图像，存储
for j = 1:length(video_dir) %视频级别；
% 弹出错误提示： Could not read file due to an unexpected error. Reason:
% Unable to initialize the video obtain properties ...
frames = VideoReader (strcat(path, video_dir(j).name));
numFrames =frames.NumberOfFrames;
for k = 1 : 30: numFrames
disp(['saving the ',num2str(k),'-th frames, please waiting....']);
frame = read(frames,k); %将frame存储起来；
frame = imresize(frame, [200, 300]); % 是否要对图像进行resize ?
imwrite(frame,[savePath,sprintf('%08d.png',k)]);
end
end
%% 读取图像，输入给 faster rcnn
image_path = savePath;
imageFile = dir([image_path, '*.png']);
for iii = 1:length(imageFile)
im = imread([image_path, imageFile(iii).name]);
if opts.use_gpu
im = gpuArray(im);
end
% test proposal
th = tic();
[boxes, scores] = proposal_im_detect(proposal_detection_model.conf_proposal, rpn_net, im);
t_proposal = toc(th);
th = tic();
aboxes = boxes_filter([boxes, scores], opts.per_nms_topN, opts.nms_overlap_thres, opts.after_nms_topN, opts.use_gpu);
t_nms = toc(th);
% test detection
th = tic();
if proposal_detection_model.is_share_feature
[boxes, scores] = fast_rcnn_conv_feat_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
rpn_net.blobs(proposal_detection_model.last_shared_output_blob_name), ...
aboxes(:, 1:4), opts.after_nms_topN);
else
[boxes, scores] = fast_rcnn_im_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
aboxes(:, 1:4), opts.after_nms_topN);
end
t_detection = toc(th);
fprintf('%s (%dx%d): time %.3fs (resize+conv+proposal: %.3fs, nms+regionwise: %.3fs)
', ['the ' num2str(j) '-th image '], size(im, 2), size(im, 1), t_proposal + t_nms + t_detection, t_proposal, t_nms+t_detection);
running_time(end+1) = t_proposal + t_nms + t_detection;
%% visualize 可视化；
classes = proposal_detection_model.classes; % 20类物体，包括行人这一类；
boxes_cell = cell(length(classes), 1); % 20*1 cell;
thres = 0.6;
for i = 1:length(boxes_cell)
boxes_cell{i} = [boxes(:, (1+(i-1)*4):(i*4)), scores(:, i)];
boxes_cell{i} = boxes_cell{i}(nms(boxes_cell{i}, 0.3), :);
I = boxes_cell{i}(:, 5) >= thres;
boxes_cell{i} = boxes_cell{i}(I, :);
end
figure(j);
[location, label, score] = output(im, boxes_cell, classes, 'voc');
if (score==0)
continue;
else
% disp(imageFile(iii).name);
save(['./mat results/' imageFile(iii).name '.mat' ], 'location', 'label', 'score', 'im');
end
showboxes(im, boxes_cell, classes, 'voc');
pause(0.1);
end
% eval(['movie_' num2str(k) '_' class_filenames{NUM} '= im_names;']);
% clear im_names;
%
% eval(['movie_bb_' num2str(k) '_' class_filenames{NUM} '= video_cell;'])
% clear video_cell;
%
% if ~exist([fullfile(pwd, 'video_frames'),'/','video.mat'],'file')
% save('./video_frames/video.mat',['movie_' num2str(k) '_' class_filenames{NUM}]);
% save('./video_frames/video_bb.mat',['movie_bb_' num2str(k) '_' class_filenames{NUM}]);
% else
% save('./video_frames/video.mat',['movie_' num2str(k) '_' class_filenames{NUM}],'-append');
% save('./video_frames/video_bb.mat',['movie_bb_' num2str(k) '_' class_filenames{NUM}], '-append');
% end
%
% eval(['clear movie_' num2str(k)]);
% eval(['clear movie_bb_' num2str(k)]);
% save('im_boxes.mat','im_cell');
fprintf('mean time: %.3fs
', mean(running_time));
caffe.reset_all();
clear mex;
end
function proposal_detection_model = load_proposal_detection_model(model_dir)
ld = load(fullfile(model_dir, 'model'));
proposal_detection_model = ld.proposal_detection_model;
clear ld;
proposal_detection_model.proposal_net_def ...
= fullfile(model_dir, proposal_detection_model.proposal_net_def);
proposal_detection_model.proposal_net ...
= fullfile(model_dir, proposal_detection_model.proposal_net);
proposal_detection_model.detection_net_def ...
= fullfile(model_dir, proposal_detection_model.detection_net_def);
proposal_detection_model.detection_net ...
= fullfile(model_dir, proposal_detection_model.detection_net);
end
function aboxes = boxes_filter(aboxes, per_nms_topN, nms_overlap_thres, after_nms_topN, use_gpu)
% to speed up nms
if per_nms_topN > 0
aboxes = aboxes(1:min(length(aboxes), per_nms_topN), :);
end
% do nms
if nms_overlap_thres > 0 && nms_overlap_thres < 1
aboxes = aboxes(nms(aboxes, nms_overlap_thres, use_gpu), :);
end
if after_nms_topN > 0
aboxes = aboxes(1:min(length(aboxes), after_nms_topN), :);
end
end